id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
Francesco/thermal-dogs-and-people-x6ejw | 2023-03-30T09:19:15.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
lengt... |
magicgh/alpaca-cleaned | 2023-04-10T07:48:32.000Z | [
"license:cc-by-4.0",
"region:us"
] | magicgh | null | null | null | 1 | 187 | ---
license: cc-by-4.0
---
|
tasksource/icl-symbol-tuning-instruct | 2023-07-26T07:20:41.000Z | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"in-context-learning",
"symbol-tuning",
"icl",
"meta-icl",
"meta-learning",
"flan",
"long-input",
"instruction... | tasksource | null | null | null | 10 | 187 | ---
license: apache-2.0
task_categories:
- text2text-generation
- text-classification
- text-generation
language:
- en
tags:
- in-context-learning
- symbol-tuning
- icl
- meta-icl
- meta-learning
- flan
- long-input
- instruction-tuning
- instruct
- metaicl
dataset_info:
features:
- name: task
dtype: string
-... |
yzhuang/autotree_automl_10000_credit_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T02:24:10.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_pmlb_10000_phoneme_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:06:07.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_automl_10000_MagicTelescope_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:48:36.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_automl_10000_MiniBooNE_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:03:38.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_automl_10000_jannis_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:07:04.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_pmlb_10000_twonorm_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:47:08.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_automl_10000_heloc_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T07:33:56.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
pavlichenko/WizardLM_evol_instruct_70k_train_val_split | 2023-09-16T12:26:29.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"region:us"
] | pavlichenko | WizardLM dataset splitted in train and validation. | null | null | 0 | 187 | ---
task_categories:
- conversational
size_categories:
- 10K<n<100K
--- |
maritaca-ai/imdb_pt | 2023-04-01T16:15:34.000Z | [
"region:us"
] | maritaca-ai | Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\ | @InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for... | null | 2 | 186 | Entry not found |
nanyy1025/covid_fake_news | 2023-02-24T01:36:24.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"language:en",
"arxiv:2011.03327",
"region:us"
] | nanyy1025 | null | null | null | 2 | 186 | ---
task_categories:
- text-classification
- zero-shot-classification
language:
- en
---
Constraint@AAAI2021 - COVID19 Fake News Detection in English
```
@misc{patwa2020fighting,
title={Fighting an Infodemic: COVID-19 Fake News Dataset},
author={Parth Patwa and Shivam Sharma and Srinivas PYKL and Vineeth Guptha and ... |
Chinese-Vicuna/guanaco_belle_merge_v1.0 | 2023-03-30T07:49:30.000Z | [
"language:zh",
"language:en",
"language:ja",
"license:gpl-3.0",
"region:us"
] | Chinese-Vicuna | null | null | null | 79 | 186 | ---
license: gpl-3.0
language:
- zh
- en
- ja
---
Thanks for [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) and [Belle Dataset](https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN)
This dataset was created by merging the above two datasets in a certain format so that t... |
pbaoo2705/processed_dataset_v2 | 2023-09-06T05:27:28.000Z | [
"region:us"
] | pbaoo2705 | null | null | null | 0 | 186 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pubid
dtype: int32
- name: question
dtype: string
- name: context
dtype: string
- name: long_answer
dtype: string
- name: final_deci... |
yzhuang/autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T03:32:07.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 186 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_automl_10000_Higgs_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:25:57.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 186 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
HuggingFaceH4/surge_instruct_llama2 | 2023-09-17T02:54:46.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 0 | 186 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: source
dtype: string
- ... |
sem_eval_2014_task_1 | 2023-01-25T14:43:53.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extend... | null | The SemEval-2014 Task 1 focuses on Evaluation of Compositional Distributional Semantic Models
on Full Sentences through Semantic Relatedness and Entailment. The task was designed to
predict the degree of relatedness between two sentences and to detect the entailment
relation holding between them. | @inproceedings{inproceedings,
author = {Marelli, Marco and Bentivogli, Luisa and Baroni, Marco and Bernardi, Raffaella and Menini, Stefano and Zamparelli, Roberto},
year = {2014},
month = {08},
pages = {},
title = {SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through... | null | 1 | 185 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions
task_categories:
- text-classification
task_ids:
- text-... |
mozilla-foundation/common_voice_2_0 | 2023-07-29T15:59:58.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | null | 0 | 185 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
br:
- 10K<n<100K
ca:
- 10K<n<100K
cnh:
- 1K<n<10K
cv:
- 1K<n<10K
cy:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 1K<n<10K
en:
- 100K<n<1M
eo:
- 10K<n<... |
rubend18/ChatGPT-Jailbreak-Prompts | 2023-08-24T18:24:29.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:zero-shot-classification",
"task_categories:table-question-answering",
"size_categories:n<1K",
"language:en",
"language:aa",
"ChatGPT",
"JailbreakPrompts",
"LanguageModeling",
... | rubend18 | null | null | null | 26 | 185 | ---
task_categories:
- question-answering
- text-generation
- fill-mask
- zero-shot-classification
- table-question-answering
language:
- en
- aa
tags:
- ChatGPT
- JailbreakPrompts
- LanguageModeling
- ArtificialIntelligence
- TextGeneration
- Dataset
- OpenAI
- Jailbreak
- Prompts
size_categories:
- n<1K
pretty_name: ... |
allocine | 2023-01-25T14:26:09.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:fr",
"license:mit",
"region:us"
] | null | Allocine Dataset: A Large-Scale French Movie Reviews Dataset.
This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr.
It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k). | @misc{blard2019allocine,
author = {Blard, Theophile},
title = {french-sentiment-analysis-with-bert},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}},
} | null | 6 | 184 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fr
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: allocine
pretty_name: Allociné
dataset... |
squad_it | 2023-04-05T13:40:37.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:it",
"license:unknown",
... | null | SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the ori... | @InProceedings{10.1007/978-3-030-03840-3_29,
author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},
editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title={Neural Learning for Question Answering in Italian},
booktitle={AI*IA 2018 -- Advances in Art... | null | 2 | 184 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- it
language_bcp47:
- it-IT
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
pape... |
keshan/wit-dataset | 2021-08-07T18:15:42.000Z | [
"region:us"
] | keshan | \\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
Its size enables WIT to be used as a pretraining dataset for multimodal machine learn... | @article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
} | null | 1 | 184 | https://github.com/google-research-datasets/wit
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
```
@article{srinivasan2021wit,
title... |
zzliang/GRIT | 2023-07-04T06:40:28.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:zero-shot-classification",
"task_ids:image-captioning",
"task_ids:visual-question-answering",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:COYO-700M"... | zzliang | null | null | null | 37 | 184 | ---
license: ms-pl
language:
- en
multilinguality:
- monolingual
pretty_name: GRIT
size_categories:
- 100M<n<1B
source_datasets:
- COYO-700M
tags:
- image-text-bounding-box pairs
- image-text pairs
task_categories:
- text-to-image
- image-to-text
- object-detection
- zero-shot-classification
task_ids:
- image-captionin... |
nampdn-ai/tiny-orca-textbooks | 2023-09-28T02:15:06.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2305.07759",
"region:us"
] | nampdn-ai | null | null | null | 5 | 184 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Orca Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A Comprehensive Resource for Text-Based Skills Development in Small Language Models
This dataset is a collection of **147k synthetic textbooks** des... |
yzhuang/autotree_pmlb_10000_magic_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:44:01.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
tyzhu/squad_v2_1000_0.50_id | 2023-09-12T16:42:18.000Z | [
"region:us"
] | tyzhu | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: id
dtype: strin... |
result-kand2-sdxl-wuerst-karlo/e8491cc1 | 2023-10-03T01:18:56.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1314
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e8491cc... |
jjonhwa/dolly-ko | 2023-10-08T09:55:19.000Z | [
"region:us"
] | jjonhwa | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 14238792
num_examples: 15011
download_size: 8006189
dataset_size: 14238792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dolly-ko"
[More Inform... |
definite_pronoun_resolution | 2023-04-05T10:04:44.000Z | [
"task_categories:token-classification",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived... | @inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
p... | null | 3 | 183 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- word-sense-disambiguation
paperswithcode_id: definite-pronoun-resolu... |
SkelterLabsInc/JaQuAD | 2022-10-25T09:06:40.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ja",
"license:cc-by-sa-3.0",
"arxiv... | SkelterLabsInc | null | null | null | 5 | 183 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- ja
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: "JaQuAD: Japanese Question Answering Dataset"
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- questio... |
yxchar/citation_intent-tlm | 2021-11-04T23:47:24.000Z | [
"region:us"
] | yxchar | null | null | null | 2 | 183 | Entry not found |
lighteval/mutual_harness | 2023-08-09T15:50:01.000Z | [
"region:us"
] | lighteval | MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is
modified from Chinese high school English listening comprehension test data. | @inproceedings{mutual,
title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
author = "Cui, Leyang and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
year = "2020",
publisher = "Asso... | null | 2 | 183 | Entry not found |
yzhuang/autotree_automl_10000_default-of-credit-card-clients_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:10:11.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 183 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
emrgnt-cmplxty/sciphi-python-textbook | 2023-10-09T15:49:57.000Z | [
"license:llama2",
"arxiv:2306.11644",
"region:us"
] | emrgnt-cmplxty | null | null | null | 30 | 183 | ---
license: llama2
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: formatted_prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 1622068845
num_examples: 555072
download_size: 753726617
... |
PORTULAN/glue-ptpt | 2023-05-12T12:49:02.000Z | [
"language_creators:machine-generated",
"size_categories:10K<n<100K",
"source_datasets:glue",
"language:pt",
"arxiv:2305.06721",
"region:us"
] | PORTULAN | GLUE-PTPT is an European Portuguese translation of the GLUE benchmark using DeepL Pro. | @misc{Gomes2023,
author = {Luís Gomes and João Rodrigues and João Silva and António Branco and Rodrigo Santos},
title = {GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face dataset},
howpu... | null | 3 | 182 | ---
language:
- pt
language_creators:
- machine-generated
source_datasets:
- glue
pretty_name: GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese
size_categories:
- 10K<n<100K
---
# GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to ... |
yzhuang/autotree_pmlb_10000_Hill_Valley_with_noise_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:14:27.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 182 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
yzhuang/autotree_pmlb_10000_Hill_Valley_without_noise_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:25:19.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 182 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
result-kand2-sdxl-wuerst-karlo/78fe0016 | 2023-10-03T01:21:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 182 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1317
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "78fe001... |
Zaid/quac_expanded | 2021-10-04T19:41:30.000Z | [
"region:us"
] | Zaid | \\nQuestion Answering in Context is a dataset for modeling, understanding,
and participating in information seeking dialog. Data instances consist
of an interactive dialog between two crowd workers: (1) a student who
poses a sequence of freeform questions to learn as much as possible
about a hidden Wikipedia text, and ... | \\n@inproceedings{choi-etal-2018-quac,
title = "QUAC: Question answering in context",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform... | null | 0 | 181 | Entry not found |
Bingsu/Human_Action_Recognition | 2022-07-05T02:48:56.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:odbl",
"region:us"
] | Bingsu | null | null | null | 7 | 181 | ---
language:
- en
license:
- odbl
pretty_name: Human Action Recognition
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
---
## Dataset Description
- **Homepage:** [Human Action Recognition (HAR) Dataset](https://www.kaggle.com/datasets/meetnagadia/human-action-recogni... |
PKU-Alignment/BeaverTails | 2023-07-20T15:33:08.000Z | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"moderation",
"rejection-sampling",
"llm",
"lm",
"human-feedback",
"arxiv:2307.04657",
"region:us"
] | PKU-Alignment | null | null | null | 11 | 181 | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- safe
- safety
- ai-safety
- moderation
- rejection-sampling
- llm
- lm
- human-feedback
size_categories:
- 100K<n<1M
---
# Dataset Card for BeaverTails
BeaverTails is an AI safety-focused collection comprising a series of datasets... |
AlignmentLab-AI/agentcode | 2023-10-10T11:53:55.000Z | [
"region:us"
] | AlignmentLab-AI | null | null | null | 2 | 181 | Entry not found |
NamCyan/thevault-docstringstyle | 2023-09-15T18:55:54.000Z | [
"region:us"
] | NamCyan | null | null | null | 0 | 181 | ---
dataset_info:
features:
- name: hexsha
dtype: string
- name: repo
dtype: string
- name: path
dtype: string
- name: license
sequence: string
- name: language
dtype: string
- name: identifier
dtype: string
- name: return_type
dtype: string
- name: original_string
dtyp... |
SneakyInsect/maestro-rollingsplit | 2023-10-04T13:21:21.000Z | [
"region:us"
] | SneakyInsect | null | null | null | 0 | 181 | ---
dataset_info:
features:
- name: name
dtype: string
- name: start
sequence: float64
- name: duration
sequence: float64
- name: pitch
sequence: float64
- name: velocity
sequence: float64
splits:
- name: train
num_bytes: 745208510
num_examples: 373963
- name: validation
... |
mwsc | 2023-04-05T13:33:22.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|winograd_wsc",
"language:en",
"license:cc-by-4.0",
"... | null | Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing. | @article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
} | null | 0 | 180 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Modified Winograd Schema Challenge (MWSC)
size_categories:
- n<1K
source_datasets:
- extended|winograd_wsc
task_categories:
- multiple-choice
task_ids:
- mul... |
zest | 2022-11-18T22:05:40.000Z | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"lang... | null | ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired... | @inproceedings{weller-etal-2020-learning,
title = "Learning from Task Descriptions",
author = "Weller, Orion and
Lourie, Nicholas and
Gardner, Matt and
Peters, Matthew",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
mon... | null | 1 | 180 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode... |
yxchar/sciie-tlm | 2021-11-05T02:04:05.000Z | [
"region:us"
] | yxchar | null | null | null | 0 | 180 | Entry not found |
reasoning-machines/gsm-hard | 2023-01-17T03:21:10.000Z | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:gsm8k (https://huggingface.co/datasets/gsm8k)",
"language:code",
"license:mit",
"math_reasoning",
"symbolic_rea... | reasoning-machines | null | null | null | 12 | 180 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- gsm8k (https://huggingface.co/datasets/gsm8k)
task_categories:
- text2text-generation
task_ids: []
pretty_name: gsm-hard
tags:
- ... |
AnonymousSub/MedQuAD_47441_Question_Answer_Pairs | 2023-03-09T15:02:29.000Z | [
"region:us"
] | AnonymousSub | null | null | null | 4 | 180 | ---
dataset_info:
features:
- name: Questions
dtype: string
- name: Answers
dtype: string
splits:
- name: train
num_bytes: 24216623
num_examples: 47441
download_size: 9258859
dataset_size: 24216623
---
# Dataset Card for "MedQuAD_47441_Question_Answer_Pairs"
[More Information needed](http... |
germank/shp_with_features_20k_flan_t5_large | 2023-05-15T08:49:22.000Z | [
"region:us"
] | germank | null | null | null | 0 | 180 | ---
dataset_info:
features:
- name: post_id
dtype: string
- name: domain
dtype: string
- name: upvote_ratio
dtype: float64
- name: history
dtype: string
- name: c_root_id_A
dtype: string
- name: c_root_id_B
dtype: string
- name: created_at_utc_A
dtype: int64
- name: created... |
abacusai/WikiQA-Free_Form_QA | 2023-07-27T14:37:54.000Z | [
"region:us"
] | abacusai | null | null | null | 7 | 180 | ---
configs:
- config_name: default
data_files:
- split: 2k
path: data/2k-*
- split: 4k
path: data/4k-*
- split: 8k
path: data/8k-*
- split: 16k
path: data/16k-*
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: tok_len
dtype: in... |
kelm | 2022-11-18T20:16:37.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"data-to-text-generation",
"arxiv:2010.12688",
"region:us"
] | null | Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 dis... | @misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primary... | null | 6 | 179 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: kelm
pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training ... |
gia-project/gia-dataset | 2023-09-05T06:36:39.000Z | [
"task_categories:reinforcement-learning",
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:found",
"annotations_creators:machine-generated",
"source_datasets:conceptual-captions",
"source_datasets:ok-vqa",
"source_datasets:oscar",
"license:apache-2.0",
... | gia-project | GIA dataset. | null | null | 1 | 179 | ---
license: apache-2.0
tags:
- imitation-learning
- reinforcement-learning
- text-generation
- question-answering
- generalist-agent
annotations_creators:
- found
- machine-generated
pretty_name: GIA-dataset
size_categories:
- {number_of_elements_in_dataset} # Example: n<1K, 100K<n<1M, …
source_datasets:
- conceptual... |
vjain/CBT | 2023-05-27T13:01:00.000Z | [
"license:openrail",
"region:us"
] | vjain | null | null | null | 0 | 179 | ---
license: openrail
---
|
medal | 2023-06-13T12:39:11.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:unknown",
"disambiguation",
"region:us"
] | null | A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate | @inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
mon... | null | 9 | 178 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: medal
pretty_name: MeDAL
tags:
- disambiguation
dataset_i... |
castorini/mr-tydi-corpus | 2022-10-12T20:25:51.000Z | [
"task_categories:text-retrieval",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] | castorini | null | null | null | 2 | 178 | ---
language:
- ar
- bn
- en
- fi
- id
- fi
- ja
- ko
- ru
- sw
- te
- th
multilinguality:
- multilingual
task_categories:
- text-retrieval
license: apache-2.0
---
# Dataset Summary
Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse l... |
bigbio/hallmarks_of_cancer | 2022-12-22T15:44:44.000Z | [
"multilinguality:monolingual",
"language:en",
"license:gpl-3.0",
"region:us"
] | bigbio | The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication
abstracts manually annotated by experts according to a taxonomy. The taxonomy
consists of 37 classes in a hierarchy. Zero or more class labels are assigned
to each sentence in the corpus. The labels are found under the "labels"
directory, while th... | @article{DBLP:journals/bioinformatics/BakerSGAHSK16,
author = {Simon Baker and
Ilona Silins and
Yufan Guo and
Imran Ali and
Johan H{\"{o}}gberg and
Ulla Stenius and
Anna Korhonen},
title = {Automatic semantic classifica... | null | 1 | 178 |
---
language:
- en
bigbio_language:
- English
license: gpl-3.0
multilinguality: monolingual
bigbio_license_shortname: GPL_3p0
pretty_name: Hallmarks of Cancer
homepage: https://github.com/sb895/Hallmarks-of-Cancer
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for H... |
ajaykarthick/imdb-movie-reviews | 2023-02-08T21:08:35.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"region:us"
] | ajaykarthick | null | null | null | 1 | 178 | ---
task_categories:
- text-classification
- token-classification
- feature-extraction
pretty_name: Movie-Reviews
size_categories:
- 10K<n<100K
---
# IMDB Movie Reviews
This is a dataset for binary sentiment classification containing substantially huge data. This dataset co... |
red_caps | 2023-01-25T14:43:07.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2111.11431",
"region:us"
] | null | RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.
Images and captions from Reddit depict and describe a wide variety of objects and scenes.
The data is collected from a manually curated set of subreddits (350 total),
which give coarse image labels and allow steering of the dataset composit... | @misc{desai2021redcaps,
title={RedCaps: web-curated image-text data created by the people, for the people},
author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
year={2021},
eprint={2111.11431},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | null | 43 | 177 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: redcaps
pretty_name: RedCaps
dataset_info:
features... |
bertin-project/alpaca-spanish | 2023-03-24T11:38:19.000Z | [
"task_categories:text-generation",
"language:es",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] | bertin-project | null | null | null | 18 | 177 | ---
license: cc-by-4.0
language:
- es
tags:
- instruction-finetuning
pretty_name: BERTIN Alpaca Spanish
task_categories:
- text-generation
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2143... |
thefcraft/civitai-stable-diffusion-337k | 2023-09-26T07:10:40.000Z | [
"annotations_creators:no-annotation",
"language_creators:thefcraft",
"size_categories:1M<n<10M",
"source_datasets:civitai",
"language:en",
"region:us"
] | thefcraft | null | null | null | 8 | 177 | ---
annotations_creators:
- no-annotation
language_creators:
- thefcraft
language:
- en
pretty_name: civitai-stable-diffusion-337k
size_categories:
- 1M<n<10M
source_datasets:
- civitai
---
### How to Use
```
from datasets import load_dataset
dataset = load_dataset("thefcraft/civitai-stable-diffusion-337k")
print(d... |
ChilleD/MultiArith | 2023-05-02T01:44:21.000Z | [
"region:us"
] | ChilleD | null | null | null | 1 | 177 | Entry not found |
jinho8345/funsd | 2023-07-29T09:06:10.000Z | [
"region:us"
] | jinho8345 | null | null | null | 0 | 177 | ---
dataset_info:
features:
- name: img
dtype: image
- name: filename
dtype: string
- name: boxes
sequence:
sequence: int64
- name: labels
sequence: string
- name: words
list:
list:
- name: box
sequence: int64
- name: text
dtype: string
- name: l... |
break_data | 2023-04-05T09:42:04.000Z | [
"task_categories:text2text-generation",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact... | @article{Wolfson2020Break,
title={Break It Down: A Question Understanding Benchmark},
author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
} | null | 0 | 176 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- open-domain-abstractive-qa
paperswithcode_id: break
pretty_name: BREAK... |
kinnews_kirnews | 2023-06-01T14:59:50.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"l... | null | Kinyarwanda and Kirundi news classification datasets | @article{niyongabo2020kinnews,
title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi},
author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li},
journal={arXiv preprint arXiv:2010.12174},
year={2020}
} | null | 1 | 176 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- rn
- rw
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
paperswithco... |
search_qa | 2023-06-16T09:03:21.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1704.05179",
"region:us"
] | null | We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind
CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an exi... | null | null | 10 | 176 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: SearchQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: searchqa
dataset_info:
- config_... |
nielsr/CelebA-faces | 2022-03-21T14:48:37.000Z | [
"region:us"
] | nielsr | null | null | null | 3 | 176 | Entry not found |
EleutherAI/pythia-memorized-evals | 2023-03-14T15:12:36.000Z | [
"region:us"
] | EleutherAI | null | null | null | 1 | 176 | ---
dataset_info:
features:
- name: index
dtype: int64
- name: tokens
sequence: int64
- name: __index_level_0__
dtype: int64
splits:
- name: duped.1.4b
num_bytes: 730820104
num_examples: 1373722
- name: deduped.1.4b
num_bytes: 557587604
num_examples: 1048097
- name: duped.160... |
vjain/Therapy | 2023-05-16T22:31:22.000Z | [
"region:us"
] | vjain | null | null | null | 1 | 176 | Entry not found |
result-kand2-sdxl-wuerst-karlo/040dec0a | 2023-10-03T08:51:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 176 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 160
num_examples: 10
download_size: 1292
dataset_size: 160
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "040dec0... |
result-kand2-sdxl-wuerst-karlo/f4d8fc49 | 2023-10-03T08:54:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 176 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 159
num_examples: 10
download_size: 1306
dataset_size: 159
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f4d8fc4... |
atasoglu/databricks-dolly-15k-tr | 2023-05-01T10:30:39.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:tr",
"license:cc-by-sa-3.0",
"region:us"
] | atasoglu | null | null | null | 6 | 175 | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
language:
- tr
pretty_name: databricks-dolly-15k-tr
size_categories:
- 10K<n<100K
---
This dataset is machine-translated version of [databricks-dolly-15k.jsonl](https://github.com/databrickslabs/dolly/tree/master/data) into Turkish.
Used `googletrans==3.1... |
Mike0307/MNIST-M | 2023-07-04T15:10:25.000Z | [
"license:mit",
"region:us"
] | Mike0307 | null | null | null | 0 | 175 | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9... |
FreedomIntelligence/CMB | 2023-08-19T09:45:53.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"medical",
"biology",
"chemistry",
"region:us"
] | FreedomIntelligence |
Chinese Medical Benchmark | coming soon~ | null | 5 | 175 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- zh
tags:
- medical
- biology
- chemistry
size_categories:
- 100K<n<1M
---
# CMB: A Comprehensive Medical Benchmark in Chinese
<p align="center">
🌐 <a href="https://cmedbenchmark.llmzoo.com/#home" t... |
kandriiashevskyi/wix_looker_ai | 2023-10-10T09:12:52.000Z | [
"region:us"
] | kandriiashevskyi | null | null | null | 0 | 175 | Entry not found |
emozilla/pg19 | 2023-10-09T15:06:39.000Z | [
"region:us"
] | emozilla | null | null | null | 3 | 175 | ---
dataset_info:
features:
- name: short_book_title
dtype: string
- name: publication_date
dtype: int32
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 11453688452
num_examples: 28602
- name: validation
num_bytes: 17402295
num_exampl... |
result-kand2-sdxl-wuerst-karlo/6a3f723d | 2023-10-03T08:48:05.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 175 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1317
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6a3f723... |
iohadrubin/smcalflow | 2022-01-01T20:57:52.000Z | [
"region:us"
] | iohadrubin | null | 2 | 174 | Entry not found | ||
ranpox/xfund | 2021-09-08T11:15:02.000Z | [
"region:us"
] | ranpox | null | null | null | 3 | 174 | Entry not found |
JeremyAlain/SLF5K | 2023-01-24T14:21:35.000Z | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"feedback",
"human feedback",
"language feedback",
"binary feedback",... | JeremyAlain | The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used for the task of abstraction summarization. Each sample consists of a Reddit title and post, a model-generated (FeedME) summary, and human-written language feedback on that summary. Addit... | @article{
} | null | 4 | 174 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: apache-2.0
multilinguality:
- monolingual
pretty_name: SLF5K
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- feedback
- human feedback
- language feedback
- binary feedback
- reward
- reward model
- gpt3
- gp... |
emozilla/govreport-test-tokenized | 2023-08-09T02:35:24.000Z | [
"region:us"
] | emozilla | null | null | null | 0 | 174 | ---
dataset_info:
features:
- name: id
dtype: string
- name: pid
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: tokenized_len
dtype: int64
splits:
- name: test
num_... |
ura-hcmut/MATH | 2023-09-29T17:19:11.000Z | [
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] | ura-hcmut | null | null | null | 0 | 174 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: gcp
data_files:
- split: train
path: "MATH_gcp_training.csv"
- split: test
path: "MATH_gcp.csv"
- config_name: azr
data_files:
- split: train
path: "MATH_azr_training.csv"
- split: test
... |
conceptual_12m | 2022-11-03T16:31:22.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2102.08981",
"region:us"
] | null | Conceptual 12M is a large-scale dataset of 12 million
image-text pairs specifically meant to be used for visionand-language pre-training.
Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M. | @inproceedings{changpinyo2021cc12m,
title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
booktitle = {CVPR},
year = {2021},
} | null | 10 | 173 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: cc12m
pretty_name: Conceptual 12M
dataset_info:
feature... |
florentgbelidji/car-reviews | 2022-06-08T16:43:39.000Z | [
"region:us"
] | florentgbelidji | null | null | null | 0 | 173 | Entry not found |
tomekkorbak/detoxify-pile-chunk3-50000-100000 | 2022-10-06T02:59:17.000Z | [
"region:us"
] | tomekkorbak | null | null | null | 0 | 173 | Entry not found |
Jean-Baptiste/financial_news_sentiment | 2022-12-29T03:14:44.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | Jean-Baptiste | null | null | null | 6 | 173 | ---
language:
- en
dataset_info:
splits:
- name: test
num_examples: 267
- name: train
num_examples: 1512
annotations_creators:
- expert-generated
license:
- mit
multilinguality:
- monolingual
pretty_name: financial_news_sentiment
size_categories:
- 1K<n<10K
tags: []
task_categories:
- text-classification
... |
Tuana/presidents | 2023-02-28T01:06:47.000Z | [
"region:us"
] | Tuana | null | null | null | 1 | 173 | ---
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: content_type
dtype: string
- name: meta
struct:
- name: url
dtype: string
- name: _split_id
dtype: int64
- name: id_hash_keys
sequence: string
- name: score
dtype: 'null'
... |
casehold/casehold | 2023-10-04T19:55:29.000Z | [
"region:us"
] | casehold | CaseHOLD (Case Holdings On Legal Decisions) is a law dataset comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case. | @inproceedings{zhengguha2021,
title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
year={2021},
eprint={2104.08671},
archivePrefix={arXiv},
primary... | null | 4 | 173 | Entry not found |
aboonaji/alpaca_micro_demo | 2023-08-08T13:57:18.000Z | [
"region:us"
] | aboonaji | null | null | null | 0 | 173 | Entry not found |
ziozzang/EverythingLM-data-V2-Ko | 2023-08-23T07:03:47.000Z | [
"language:ko",
"license:mit",
"region:us"
] | ziozzang | null | null | null | 7 | 173 | ---
license: mit
language:
- ko
---
# Translated into Korean with DeepL
All Texts are translated with DeepL. (Machine Translated.)
- Issue: some data items are missing, cause of DeepL plan and processing method. I use very cheap plan and all datas are merged into single file and splitted by few code and hand.
- This... |
vlsp-2023-vllm/arithmetic_vi | 2023-09-19T03:54:17.000Z | [
"arxiv:2005.14165",
"region:us"
] | vlsp-2023-vllm | null | null | null | 0 | 173 | ---
dataset_info:
features:
- name: context
dtype: string
- name: completion
dtype: string
- name: meta
dtype: string
splits:
- name: test
num_bytes: 1729595
num_examples: 26000
download_size: 515170
dataset_size: 1729595
---
# Arithmetic (OpenAI)
Source: https://github.com/openai/g... |
NamCyan/Evol-TheVault | 2023-09-15T18:04:10.000Z | [
"region:us"
] | NamCyan | null | null | null | 0 | 173 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: instruction
dtype: string
- name: code
dtype: string
- name: tokenized_instruction
sequence: string
- name: type
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 175466743
num_examp... |
great_code | 2022-11-18T20:05:00.000Z | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr]
This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensi... | @inproceedings{DBLP:conf/iclr/HellendoornSSMB20,
author = {Vincent J. Hellendoorn and
Charles Sutton and
Rishabh Singh and
Petros Maniatis and
David Bieber},
title = {Global Relational Models of Source Code},
booktitle = {8th International Confere... | null | 1 | 172 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: null
pretty_name: GREAT
dataset_info:
features:
- nam... |
flexthink/librig2p-nostress-space | 2022-06-24T01:23:49.000Z | [
"region:us"
] | flexthink | Grapheme-to-Phoneme training, validation and test sets | null | null | 0 | 172 | # librig2p-nostress - Grapheme-To-Phoneme Dataset
This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
The dataset is derived from the following pre-existing datasets:
* [LibriSpeech ASR Corpus](https://www.openslr.org/12)
* [LibriSpeech Alignments](htt... |
eloukas/edgar-corpus | 2023-07-14T07:17:12.000Z | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:en",
"license:apache-2.0",
"research papers",
"edgar",
"sec",
"finance",
"financial",
"filings",... | eloukas | The dataset contains annual filings (10K) of all publicly traded firms from 1993-2020. The table data is stripped but all text is retained.
This dataset allows easy access to the EDGAR-CORPUS dataset based on the paper EDGAR-CORPUS: Billions of Tokens Make The World Go Round (See References in README.md for details). | null | null | 16 | 172 | ---
dataset_info:
- config_name: .
features:
- name: filename
dtype: string
- name: cik
dtype: string
- name: year
dtype: string
- name: section_1
dtype: string
- name: section_1A
dtype: string
- name: section_1B
dtype: string
- name: section_2
dtype: string
- name: section... |
commaai/commavq | 2023-09-19T21:38:38.000Z | [
"size_categories:100K<n<1M",
"license:mit",
"region:us"
] | commaai | TODO | null | null | 9 | 172 | ---
license: mit
size_categories:
- 100K<n<1M
---
# commaVQ
commaVQ is a dataset of 100,000 heavily compressed driving videos for Machine Learning research. A heavily compressed driving video like this is useful to experiment with GPT-like video prediction models. This repo includes an encoder/decoder and an example o... |
jason-lee08/TinyStoriesWithExclamationsSmall | 2023-08-20T03:52:44.000Z | [
"region:us"
] | jason-lee08 | null | null | null | 0 | 172 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 23826331
num_examples: 21... |
catalonia_independence | 2023-06-01T14:59:47.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ca",
"language:es",
"license:cc-by-nc-sa-4.0",
"stance-detection",
"region:us"
] | null | This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
... | @inproceedings{zotova-etal-2020-multilingual,
title = "Multilingual Stance Detection in Tweets: The {C}atalonia Independence Corpus",
author = "Zotova, Elena and
Agerri, Rodrigo and
Nunez, Manuel and
Rigau, German",
booktitle = "Proceedings of the 12th Language Resources and Evaluation ... | null | 1 | 171 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ca
- es
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: cic
pretty_name: Catalonia Indepen... |
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